{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T12:34:11Z","timestamp":1782390851404,"version":"3.54.5"},"reference-count":27,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,6,1]]},"abstract":"<jats:p>Fault diagnosis of rapier loom is an inevitable requirement to meet the demand of intelligent manufacturing. Facing the strong noise interference caused by complex working environment, accurate and reliable vibration signal detection of blade loom spindle is the key to realize the rapier loom fault diagnosis. This paper proposes a method to extract the spindle vibration signal of the rapier loom by Adaptive Piecewise Hybrid Stochastic Resonance (APHSR) after the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). Firstly, ICEEMDAN is used to pre-process the weak vibration signal containing noise, decompose the signal into multiple IMF components and display the high and low frequency signal characteristics of the original signal. Then, the energy density method and the correlation coefficient method are used to remove high and low noise, respectively, to filter the optimal IMF components, and then the signal containing valid information is reconstructed. Finally, the reconstructed signal is input to APHSR for noise-assisted enhancement after scale transformation to restore the faint vibration signal feature frequencies and achieve effective feature extraction. Through the simulation experiment and the engineering fault experiment analysis, comparing ICEEMDAN-APHSR with CEEMDAN-SR, ICEEMDAN-SR, CEEMDAN-APHSR methods. The difference between the spectrum amplitude, the spectrum amplitude and the maximum noise and the maximum signal to noise ratio (SNR) of the fault feature frequency of the rapier loom spindle bearing increased by 3.3668\u200adB,1.7205\u200adB,2.3952\u200adB, respectively. The results show that ICEEMDAN-APHSR method can accurately extract the fault feature frequency of the spindle bearing of rapier loom, and effectively solves the problem of extracting the weak vibration signal feature of rapier loom in the background of strong noise. This method is beneficial to the future research of rapier loom fault diagnosis, and is of great significance to promote the maintenance of loom equipment and production safety and quality.<\/jats:p>","DOI":"10.3233\/jifs-223664","type":"journal-article","created":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T11:13:27Z","timestamp":1685445207000},"page":"9203-9230","source":"Crossref","is-referenced-by-count":2,"title":["Research on feature extraction method of spindle vibration detection of weak signals for rapier loom fault diagnosis in strong noise background"],"prefix":"10.1177","volume":"44","author":[{"given":"Yanjun","family":"Xiao","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Hebei University of Technology, Tianjin, China"},{"name":"Career Leader Intelligent Control Automation Company, Suqian, Jiangsu Province, China"},{"name":"Tianjin Key Lab Power Transmiss & Safety Technol, Department State Key Lab Reliabil & Intellectual Elect, Tianjin, Peoples R China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Hebei University of Technology, Tianjin, China"},{"name":"Career Leader Intelligent Control Automation Company, Suqian, Jiangsu Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeyu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Hebei University of Technology, Tianjin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Wan","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Hebei University of Technology, Tianjin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"issue":"7","key":"10.3233\/JIFS-223664_ref1","doi-asserted-by":"crossref","first-page":"1825","DOI":"10.1016\/j.automatica.2014.04.006","article-title":"Fault-tolerant control of Markovian jump stochastic systems via the augmented sliding mode observer approach[J]","volume":"50","author":"Li","year":"2014","journal-title":"Automatica"},{"key":"10.3233\/JIFS-223664_ref2","first-page":"142","article-title":"Sliding mode-based adaptive resilient control for Markovian jump cyber-physical systems in face of simultaneous actuator and sensor attacks[J]","author":"Yang","year":"2022","journal-title":"Automatica"},{"issue":"6","key":"10.3233\/JIFS-223664_ref3","doi-asserted-by":"publisher","first-page":"3687","DOI":"10.1109\/TSMC.2020.3004659","article-title":"Neural Network-Based Adaptive Fault-Tolerant Control for Markovian Jump Systems With Nonlinearity and Actuator Faults","volume":"51","author":"Yang","year":"2021","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"key":"10.3233\/JIFS-223664_ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TTE.2022.3204843"},{"key":"10.3233\/JIFS-223664_ref5","doi-asserted-by":"crossref","first-page":"118795","DOI":"10.1016\/j.apenergy.2022.118795","article-title":"An adaptive boosting charging strategy optimization based on thermoelectric-aging model, surrogates and multi-objective optimization[J]","volume":"312","author":"Su","year":"2022","journal-title":"Applied Energy"},{"issue":"4-5","key":"10.3233\/JIFS-223664_ref6","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1016\/j.jsv.2007.01.006","article-title":"Fault diagnosis of rotating machinery based on auto-associative neural networks and wavelet transforms [J]","volume":"302","author":"Sanz","year":"2007","journal-title":"Journal of Sound and Vibration"},{"issue":"4","key":"10.3233\/JIFS-223664_ref7","doi-asserted-by":"crossref","first-page":"2937","DOI":"10.1016\/j.eswa.2007.05.011","article-title":"Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition [J]","volume":"34","author":"Ekici","year":"2008","journal-title":"Expert Systems with Applications"},{"key":"10.3233\/JIFS-223664_ref8","unstructured":"Ren Q. , Research on fault diagnosis of rotating machinery based on vibration signal analysis, M.S. Qingdao University, China, 2021."},{"key":"10.3233\/JIFS-223664_ref9","unstructured":"Jiang H.S. , Noise vibration analysis and vibration reduction of rapier loom, Noise and vibration control (4) (1993), pp. 17\u201319+16, 12."},{"key":"10.3233\/JIFS-223664_ref10","unstructured":"Guo J.J. , Anti interference in mechanical vibration testing system, Modern machinery (5) (2007), pp. 19\u201321+33."},{"key":"10.3233\/JIFS-223664_ref11","doi-asserted-by":"publisher","DOI":"10.13382\/j.jemi.2018.01.008"},{"key":"10.3233\/JIFS-223664_ref12","doi-asserted-by":"publisher","DOI":"10.13382\/j.jemi.2017.01.004"},{"key":"10.3233\/JIFS-223664_ref13","unstructured":"Zhang Y. , Bearing fault feature extraction based on EMD and wavelet packet, Information and Electronic Engineering 10(5) (2012), pp. 330\u2013333+338."},{"issue":"4","key":"10.3233\/JIFS-223664_ref14","first-page":"67","article-title":"Research on gearbox feature extraction technology based on EMD and fractal","volume":"52","author":"Lou","year":"2014","journal-title":"Machine Building"},{"key":"10.3233\/JIFS-223664_ref15","doi-asserted-by":"publisher","DOI":"10.16450\/j.cnki.issn.1004-6801.2016.03.018"},{"key":"10.3233\/JIFS-223664_ref16","unstructured":"Zhu Q. and Zhu Y. , CEEMDAN auxiliary fast spectrum cliff degree of rolling bearing fault diagnosis method, Light Machinery 40(7) (2022), pp. 74\u201379+84."},{"key":"10.3233\/JIFS-223664_ref17","doi-asserted-by":"publisher","DOI":"10.19533\/j.issn1000-3762.2021.10.012"},{"key":"10.3233\/JIFS-223664_ref18","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.bspc.2014.06.009","article-title":"Improved complete ensemble EMD: A suitable tool for biomedical signal processing","volume":"14","author":"Colominas","year":"2014","journal-title":"Biomedical Signal Processing and Control"},{"key":"10.3233\/JIFS-223664_ref19","first-page":"1","article-title":"Planetary gearbox fault diagnosis using ICEEMDAN and SVM","author":"Wang","year":"2022","journal-title":"Mechanical Science and Technology"},{"key":"10.3233\/JIFS-223664_ref20","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.measurement.2015.05.007","article-title":"Research of weak fault feature information extraction of planetary gear based on ensemble empirical mode decomposition and adaptive stochastic resonance","volume":"73","author":"Chen","year":"2015","journal-title":"Measurement"},{"key":"10.3233\/JIFS-223664_ref21","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.ymssp.2018.12.032","article-title":"Applications of stochastic resonance to machinery fault detection: A review and tutorial","volume":"122","author":"Qiao","year":"2019","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.3233\/JIFS-223664_ref22","doi-asserted-by":"publisher","DOI":"10.1109\/5.726785"},{"issue":"26","key":"10.3233\/JIFS-223664_ref23","doi-asserted-by":"crossref","first-page":"7386","DOI":"10.1016\/j.jsv.2014.08.039","article-title":"Adaptive bistable stochastic resonance and its application in mechanical fault feature extraction","volume":"333","author":"Qin","year":"2014","journal-title":"Journal of Sound and Vibration"},{"key":"10.3233\/JIFS-223664_ref24","unstructured":"Chen Y.F. and Hong H.C. , Principle and use of sword pole loom, China National Textile Press, 1994."},{"issue":"07","key":"10.3233\/JIFS-223664_ref25","doi-asserted-by":"publisher","first-page":"1094","DOI":"10.13374\/j.issn2095-9389.2017.07.016","article-title":"Condition warning and maintenance optimization of rotating machinery and equipment","volume":"39","author":"Zhang","year":"2017","journal-title":"Journal of Engineering Science"},{"key":"10.3233\/JIFS-223664_ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2987835"},{"key":"10.3233\/JIFS-223664_ref27","first-page":"5","article-title":"Stochastic resonance in a bistable system","author":"Fauve","journal-title":"Physics Letters A"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-223664","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:45:21Z","timestamp":1777455921000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-223664"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,1]]},"references-count":27,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.3233\/jifs-223664","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,1]]}}}